README.md

metadata

title: RAG System Demo
emoji: 🤗
colorFrom: blue
colorTo: purple
sdk: streamlit
sdk_version: 1.28.1
app_file: app.py
pinned: false

RAG System Demo

A fully functional Retrieval-Augmented Generation (RAG) system built with open-source Hugging Face models. Upload your documents, ask questions, and get AI-generated answers grounded in your content -- with full source attribution.

What It Does

This demo implements a complete RAG pipeline:

1. Document Ingestion -- Upload PDF, TXT, DOCX, or CSV files. Text is extracted and split into overlapping chunks.
2. Semantic Indexing -- Chunks are embedded with a sentence-transformer model and stored in an in-memory ChromaDB vector store.
3. Retrieval -- When you ask a question, the most semantically similar chunks are retrieved using cosine similarity.
4. Generation -- Retrieved context is passed to a language model that generates a grounded answer.

Features

• Multi-format document upload (PDF, TXT, DOCX, CSV)
• Semantic search with relevance scoring
• AI-powered question answering over your documents
• Source attribution with similarity scores
• Chat-style interface with conversation history
• Sample document included for quick testing

Models Used

Component	Model	Purpose
Text Generation	google/flan-t5-small	Instruction-following seq2seq model for Q&A
Embeddings	sentence-transformers/all-MiniLM-L6-v2	Dense vector embeddings for semantic search
Vector Store	ChromaDB (in-memory)	Fast approximate nearest neighbor search

Main Repository

This Hugging Face Space is a live demo for the full RAG System project:

https://github.com/Phoenixak99/RAG-System

Running Locally

# Clone the repository
git clone https://github.com/Phoenixak99/RAG-System.git
cd RAG-System/hf_space

# Install dependencies
pip install -r requirements.txt

# Run the Streamlit app
streamlit run app.py

Or use Docker:

docker build -t rag-demo .
docker run -p 8501:8501 rag-demo

Then open http://localhost:8501 in your browser.

Architecture

User Query
    |
    v
[Embedding Model] --> Query Vector
    |
    v
[ChromaDB] --> Top-K Similar Chunks
    |
    v
[flan-t5-small] --> Generated Answer + Source Attribution

License

MIT License -- see the main repository for details.